Is there a direct relationship between inference speed and computing power?

Inference speed and computing power generally exhibit a positive correlation, meaning higher computing power typically results in faster inference times. However, this relationship is not perfectly linear or direct.

Computing power, especially processor performance and accelerator capabilities (like GPUs/TPUs), is a primary factor determining how quickly calculations are performed. Sufficient compute resources enable parallel processing and reduce processing latency. Nevertheless, factors such as memory bandwidth, data transfer speeds, model architecture complexity, and software optimization efficiency significantly influence actual inference speed. Increasing computing power alone may yield diminishing returns if other system components become bottlenecks. Model quantization and pruning can achieve faster speeds even without more computing power.

To optimize inference in practice, balance hardware upgrades with algorithmic improvements. Assess bottlenecks first—if compute is the primary constraint, then increasing processing power directly boosts speed. In scenarios with tight latency requirements, specialized hardware accelerators are valuable. However, prioritize model optimization and efficient runtime frameworks to maximize existing computing power, often delivering significant speed gains cost-effectively before scaling hardware.